| 世古家出 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | | | . | > | | | . | | | | 申请① 2025-07-22 | 母情公布团 2025-08-22 | 授权(1) | | 而生引力() 2045-07-22 | | | 基本信息 | | | | 法律状态 | | | | 电商号 | CN202511009125 5 | 申请日 | 2025-07-22 | ● 2025-08-22 ■ | | | | 由音公布号 | CN120521612A | 申请公布日 | 2025-08-22 | 公布详情 | | | | 原因公益号 . | | 積極公告目 . | | | | | | 快出版号(1) | | 优先级目 1 | | | | | | 中新农品 | G01C21/20:G01C21/01/G06T7/136 | CPC AND . | | | | | | 문의 원칙 | 使用公布 | 受阻系 | | | | | | 以本國出著題 | 亚 | 法律状 | 中国公开 | | | | | 文 (含色权)人 | 标 ...

Core Viewpoint - The article discusses job opportunities in the fields of multimodal large models, reinforcement learning, and navigation, highlighting positions in a unicorn company with ample funding [1]. Group 1: Multimodal Large Models - Job locations are in Beijing and Shenzhen with a salary range of 40k-80k/month [2]. - Responsibilities include developing cutting-edge algorithms for embodied intelligent multimodal large models applicable in various indoor and outdoor scenarios, focusing on framework design, model optimization, and training for navigation and operation tasks [2]. - Candidates should have a master's degree or higher in computer science, artificial intelligence, robotics, or control engineering, along with extensive experience in robot perception, navigation, and AI large models [3]. - Preferred qualifications include experience with algorithms related to multimodal large models in robot navigation and a solid foundation in algorithm development and engineering implementation [3][4]. Group 2: Reinforcement Learning - Job location is in Beijing with a salary range of 40k-80k/month [5]. - Specific job descriptions and requirements are not detailed in the provided text [5]. Group 3: Embodied Navigation Algorithms - Job location is in Shenzhen with a salary range of 30k-60k/month [6]. - The role involves researching and developing algorithms for embodied intelligence, focusing on the integration of multimodal data into planning and achieving end-to-end mapping from data to actions [6]. Group 4: Additional Qualifications - Candidates should have a strong foundation in machine learning, deep learning, and reinforcement learning, with the ability to conduct independent research in embodied intelligence and related fields [7]. - Experience in publishing papers in top conferences and journals is a plus, along with strong coding skills and participation in robotics competitions [7].

Core Viewpoint - The article discusses the challenges faced by traditional navigation systems in mobile robotics and introduces ByteDance's innovative dual-model architecture, Astra, which aims to enhance navigation capabilities in complex indoor environments [2][4]. Group 1: Traditional Navigation Challenges - Mobile robots must address three core navigation challenges: goal localization, self-localization, and path planning, which are critical for safe and reliable movement in complex environments [3]. - Traditional navigation systems often rely on multiple modules and small models, which can be inefficient and require further exploration for effective integration [3]. Group 2: Astra Dual-Model Architecture - Astra consists of two sub-models: Astra-Global for low-frequency tasks like goal and self-localization, and Astra-Local for high-frequency tasks such as local path planning and odometry estimation [5]. - Astra-Global utilizes a multimodal large language model (MLLM) to process visual and language inputs for precise localization on a global map [8][10]. Group 3: Astra-Global Functionality - Astra-Global employs a two-stage process for visual-language localization, achieving high accuracy in identifying locations based on visual inputs and natural language instructions [11][12]. - The model's training involves diverse datasets and a reward-based optimization approach, resulting in a significant improvement in localization accuracy, achieving 99.9% in unseen environments compared to 93.7% with traditional methods [12]. Group 4: Astra-Local Functionality - Astra-Local is designed for efficient local path generation and odometry estimation, incorporating a 4D spatiotemporal encoder and a planning head [13][15]. - The planning head utilizes a transformer-based flow matching method to generate executable trajectories while minimizing collision rates through a mask ESDF loss approach [16][23]. Group 5: Experimental Validation - Extensive experiments in various indoor environments, including warehouses and offices, validate Astra's innovative architecture and algorithm effectiveness [19]. - Astra-Global demonstrates superior multimodal localization capabilities, significantly outperforming traditional visual place recognition methods in accuracy and robustness [20][23]. Group 6: Future Prospects - Astra has potential applications in diverse environments such as shopping malls, hospitals, and libraries, enhancing service efficiency and user experience [25]. - Future improvements are planned for Astra-Global's semantic detail retention and the introduction of active exploration mechanisms to enhance localization robustness in complex settings [25][26].

Group 1 - The core viewpoint of the article is the launch of a mentoring program for students aiming to publish papers in top conferences such as CVPR and ICRA, building on last year's successful outcomes [1] - The mentoring directions include multimodal large models, VLA, robot navigation, robot grasping, embodied generalization, embodied synthetic data, end-to-end embodied intelligence, and 3DGS [2] - The mentors have published papers in top conferences like CVPR, ICCV, ECCV, ICLR, RSS, ICML, and ICRA, indicating their rich guiding experience [3] Group 2 - Students are required to submit a resume and must come from a domestic top 100 university or an international university ranked within QS 200 [4][5]